The field of robotics is witnessing significant advancements in robot manipulation and tool usage, with a focus on improving generalization, sample efficiency, and adaptability. Researchers are exploring novel frameworks and approaches that enable robots to learn and perform complex tasks, such as hierarchical skill learning, environment-adaptive grasping, and dexterous manipulation. The integration of foundation models and force feedback is also being investigated to enhance generalization and robustness. Furthermore, the development of adaptive inverse kinematics frameworks and simulated embodiment extensions is expanding the capabilities of robots to manipulate tools of varying lengths and perform tasks in diverse environments. Noteworthy papers include: Generalizable Hierarchical Skill Learning via Object-Centric Representation, which presents a novel framework for hierarchical policy learning that improves policy generalization and sample efficiency. OmniDexGrasp: Generalizable Dexterous Grasping via Foundation Model and Force Feedback, which achieves omni-capabilities in user prompting, dexterous embodiment, and grasping tasks by combining foundation models with transfer and control strategies.